Search Results for "indicators"
Sort By:
15 projects for "indicators" with 2 filters applied:
-
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
-
Cut Data Warehouse Costs by 54%BigQuery delivers 54% lower TCO with exabyte scale and flexible pricing. Free migration tools handle the SQL translation automatically.
-
1
thinking-orbs
Dotted thought-orb loading indicators for AI & agent UIs
thinking-orbs is a small React package that provides animated dotted status indicators for AI assistants and agent interfaces. It includes nine hand-tuned states representing activities such as working, searching, solving, listening, connecting, weaving, composing, breathing, and shaping. Each animation is drawn with a plain 2D canvas rather than WebGL, SVG filters, or expensive visual effects. Two independently tuned sizes target chat-avatar and inline-text use instead of simply scaling one design. ... -
2
uqlm
Uncertainty Quantification for Language Models, is a Python package
...UQLM also supports ensemble strategies and model-as-judge approaches for evaluating responses. By combining multiple uncertainty metrics, the system provides more reliable indicators of when language model outputs may be unreliable. -
3
NOFX
Open source AI trading OS for autonomous multi-model trading systems
NOFX is an open source AI-powered trading operating system designed to automate financial trading workflows using autonomous AI agents. It acts as an infrastructure layer that transforms market data into AI-driven trade decisions and execution. Instead of requiring users to manually configure machine learning models, data sources, and API integrations, the system allows AI components to perceive market conditions, select models, and perform trading actions automatically. It supports running... -
4
InvestBrain
LLM-enabled investment tracker that consolidates market performance
...The interface blends real-time or near-real-time market data with personalized analytics, so users can assess portfolio health, diversification, and risk exposure with intuitive charts and tables. Beyond tracking, the platform offers educational insights and indicators (like technical or fundamental signals) that can inform investment decisions and help users recognize patterns or opportunities. Portfolios can be synced across devices, and users can set alerts for threshold events such as price moves or allocation shifts. -
Build Securely on AWS with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
-
5
OpenClaw Office
OpenClaw Office is the visual monitoring and management frontend
OpenClaw Office is a visual monitoring and management interface designed for the OpenClaw multi-agent system, providing an immersive and interactive way to observe and control autonomous AI agents. It presents agent activity through a virtual office environment, where each agent is represented as an animated entity within a 2D or 3D workspace. The platform enables real-time visualization of agent states, interactions, and workflows, making complex multi-agent coordination easier to... -
6
AI-Trader
100% Fully-Automated Agent-Native Trading
AI-Trader is an open-source AI-powered quantitative trading framework designed to combine financial analysis, machine learning, and autonomous trading workflows into a unified research platform. The project integrates large language models, financial indicators, market analysis pipelines, and automated decision-making systems to support strategy generation and market prediction tasks. It is built to help researchers and developers experiment with AI-assisted trading strategies using historical and real-time financial data. The framework supports modular components for portfolio analysis, signal generation, market reasoning, and trading automation, allowing users to customize workflows for different trading styles and asset classes. ... -
7
ClaudeBar
A macOS menu bar application that monitors AI coding assistant usage
...Rather than constantly running CLI commands or navigating web dashboards, users can glance at their quota statistics for services like Claude, Codex, Gemini, GitHub Copilot, and Antigravity directly from the menu bar. The application provides real-time tracking of session, weekly, and model-specific usage percentages, using visual indicators such as color-coded progress bars to communicate when quotas are healthy, nearing limits, or depleted. It includes options to enable or disable monitoring for individual providers, supports multiple visual themes (including dark mode and a festive theme), and refreshes data at configurable intervals so users always have up-to-date information. -
8
FinGPT
Open-Source Financial Large Language Models
FinGPT is an open-source, finance-specialized large language model framework that blends the capabilities of general LLMs with real-time financial data feeds, domain-specific knowledge bases, and task-oriented agents to support market analysis, research automation, and decision support. It extends traditional GPT-style models by connecting them to live or historical financial datasets, news APIs, and economic indicators so that outputs are grounded in relevant and recent market conditions rather than generic knowledge alone. The platform typically includes tools for fine-tuning, context engineering, and prompt templating, enabling users to build specialized assistants for tasks like sentiment analysis, earnings summary generation, risk profiling, trading signal interpretation, and document extraction from financial reports. -
9
FinRobot
An Open-Source AI Agent Platform for Financial Analysis using LLMs
FinRobot is an open-source AI framework focused on automating financial data workflows by combining data ingestion, feature engineering, model training, and automated decision-making pipelines tailored for quantitative finance applications. It provides developers and quants with structured modules to fetch market data, process time series, generate technical indicators, and construct features appropriate for machine learning models, while also supporting backtesting and evaluation metrics to measure strategy performance. Built with modularity in mind, FinRobot allows users to plug in custom models — from classical algorithms to deep learning architectures — and orchestrate components in pipelines that can run reproducibly across experiments. ... -
$300 Free Credits to Build on Google CloudPut your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
-
10
fastquant
Backtest and optimize your ML trading strategies with only 3 lines
...It integrates historical market data sources and trading frameworks so that users can quickly build experiments without constructing complex data pipelines. The framework enables users to test common strategies such as moving average crossovers, momentum trading, and custom indicators on historical stock data. By automating data retrieval, strategy evaluation, and result visualization, the library reduces the barrier to entry for individuals interested in quantitative finance. The project also supports optimization workflows that allow users to search for parameter combinations that improve trading strategy performance. -
11
SGX-Full-OrderBook-Tick-Data-Trading
Providing the solutions for high-frequency trading (HFT) strategies
...The repository analyzes tick-level order book data from the Singapore Exchange and attempts to capture the dynamics of limit order book movements. By extracting features such as order depth ratios and price movement indicators, the system trains machine learning models to predict short-term market changes. Several algorithms are used during model selection, including Random Forest, Extra Trees, AdaBoost, Gradient Boosting, and Support Vector Machines. The project evaluates models by predicting price direction within very short time windows and then applying a simple trading strategy based on those predictions. ... -
12
MachineLearningStocks
Using python and scikit-learn to make stock predictions
...The project provides a structured workflow that collects financial data, processes features, trains predictive models, and evaluates trading strategies. Using libraries such as pandas and scikit-learn, the repository shows how historical financial indicators can be transformed into machine learning features. The model attempts to predict whether specific stocks will outperform a benchmark index such as the S&P 500. The repository includes scripts for parsing financial statistics, building training datasets, and performing backtesting to evaluate model performance over historical periods. ... -
13
surpriver
Find big moving stocks before they move using machine learning
...The system analyzes historical stock price and volume data to detect anomalies that could indicate potential trading opportunities. By applying machine learning techniques to market indicators, the tool attempts to identify patterns in trading behavior that deviate significantly from normal market activity. These anomalies are interpreted as signals that a stock may soon experience a major upward or downward move. The framework includes modules for retrieving market data, computing technical indicators, and applying anomaly detection algorithms to identify unusual patterns. ... -
14
AIAlpha
Use unsupervised and supervised learning to predict stocks
...It provides a research-oriented environment where users can experiment with data processing pipelines, model training workflows, and quantitative trading strategies. The project typically involves collecting market data, transforming financial indicators into machine learning features, and training models to identify patterns that may predict market trends. It also demonstrates how models can be evaluated through backtesting frameworks that simulate how a strategy would perform using historical market conditions. By combining financial analytics with machine learning algorithms, the repository illustrates the process of building data-driven investment strategies. -
15
bulbea
Deep Learning based Python Library for Stock Market Prediction
bulbea is an open-source Python library designed for financial analysis and stock market prediction using machine learning and deep learning techniques. The library provides tools for retrieving financial time series data, preprocessing market data, and training predictive models that estimate future price movements. bulbea integrates common machine learning frameworks such as TensorFlow and Keras to build neural network models capable of learning patterns in historical financial data. It...
- Previous
- You're on page 1
- Next
